docstr: Tests the Jacobian of the linear_coefficients function.
docstr_tok: ["Tests","the","Jacobian","of","the","linear_coefficients","function","."]
code: def test_linear_coefficients_jacobian_random(self, size):
      """Tests the Jacobian of the linear_coefficients function."""
      tensor_shape = np.random.randint(size, 6, size=3)
      matte_init = np.random.uniform(
          0.0, 1.0, size=tensor_shape.tolist() + [1]
      )
      tensor_shape[1:3] -= size - 1
      num_coeffs = np.random.randint(2, 4)
      pseudo_inverse_init = np.random.uniform(
          0.0, 1.0, size=tensor_shape.tolist() + [num_coeffs, size**2]
      )
  
      def a_fn(matte, pseudo_inverse):
          a, _ = matting.linear_coefficients(matte, pseudo_inverse)
          return a
  
      def b_fn(matte, pseudo_inverse):
          _, b = matting.linear_coefficients(matte, pseudo_inverse)
          return b
  
      with self.subTest(name="a"):
          self.assert_jacobian_is_correct_fn(
              a_fn, [matte_init, pseudo_inverse_init]
          )
      with self.subTest(name="b"):
          self.assert_jacobian_is_correct_fn(
              b_fn, [matte_init, pseudo_inverse_init]
          )
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow
  from tensorflow_graphics.image import matting
  from tensorflow_graphics.util import asserts
  from tensorflow_graphics.util import shape
  from tensorflow_graphics.util import test_case
label_desc: Use image node_id 6 for calling the MattingTest obj's underlying member method code with example usage: obj.test_linear_coefficients_jacobian_random(size) and returns: a, b